We investigate the theoretical and empirical relationships between activity
in on-chain markets and pricing in off-chain cryptocurrency markets (e.g.,
ETH/USD prices). The motivation is to develop methods for proxying off-chain
market data using data and computation that is in principle verifiable on-chain
and could provide an alternative approach to blockchain price oracles. We
explore relationships in PoW mining, PoS validation, block space markets,
network decentralization, usage and monetary velocity, and on-chain liquidity
pools and AMMs. We select key features from these markets, which we analyze
through graphical models, mutual information, and ensemble machine learning
models to explore the degree to which off-chain pricing information can be
recovered entirely on-chain. We find that a large amount of pricing information
is contained in on-chain data, but that it is generally hard to recover precise
prices except on short time scales of retraining the model. We discuss how even
a noisy trustless data source such as this can be helpful toward minimizing
trust requirements of oracle designs